code: def _parallel_decision_function(
      n_estimators,
      clfs,
      approximators,
      X,
      total_n_estimators,
      rp_transformers,
      approx_flags,
      verbose,
  ):
      X = check_array(X)
  
      pred = []
      for i in range(n_estimators):
          estimator = clfs[i]
          if verbose > 1:
              print(
                  "predicting with estimator %d of %d for this parallel run "
                  "(total %d)..."
                  % (i + 1, n_estimators, total_n_estimators)
              )
  
          # project matrix
          X_scaled = jl_transform(X, rp_transformers[i])
  
          # turn approximator scores to labels by outlier
          if approx_flags[i] == 1:
              predicted_scores = approximators[i].predict(X_scaled)
          else:
              predicted_scores = estimator.decision_function(X_scaled)
  
          pred.append(predicted_scores)
  
      return pred
  
file_path: SUOD/suod/models/parallel_processes.py
imports: from sklearn.base import clone
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import score_to_label
  from .jl_projection import jl_fit_transform, jl_transform
  from ..utils.utility import raw_score_to_proba
label_desc: Use image node_id 7 for calling a global function with example usage: _parallel_decision_function(n_estimators, clfs, approximators, X, total_n_estimators, rp_transformers, approx_flags, verbose) and returns: pred